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EFFECTIVE ESTIMATION OF CONTEXT SIMILARITY: A PROPOSED MATCHING MODEL BASED ON WEIGHTED SEMANTIC LOAD

ISSN : 0975-900X
Journal from gdlhub / 2017-08-14 11:52:34
Oleh : Mehdi Mohammadi 1 and S.M. Fakhrahmad 2, International Journal of Artificial Intelligence & Applications
Dibuat : 2012-07-03, dengan 1 file

Keyword : Semantic Load, Context Similarity, Machine Translation, Example Based Machine Translation
Subjek : EFFECTIVE ESTIMATION OF CONTEXT SIMILARITY: A PROPOSED MATCHING MODEL BASED ON WEIGHTED SEMANTIC LOAD
Url : http://airccse.org/journal/ijaia/papers/3312ijaia01.pdf
Sumber pengambilan dokumen : Internet

In this paper, we propose a new model to calculate the similarity of two sentences. The proposed scheme is


based on the amount of semantic load which is shared between two sentences. Since verb is the essential


part of a sentence, the main focus of the proposed model is on the verbs of two sentences. We supposed the


verb as the anchor of the sentence which carries the most semantic of the sentence. The proposed model


depends on part of speech (POS), the partial order of words in the sentence and the words’ senses. The


results by Precision and Recall are promising and benchmarks show that the proposed method improves


the quality of the retrieved matched sentences.

Deskripsi Alternatif :

In this paper, we propose a new model to calculate the similarity of two sentences. The proposed scheme is


based on the amount of semantic load which is shared between two sentences. Since verb is the essential


part of a sentence, the main focus of the proposed model is on the verbs of two sentences. We supposed the


verb as the anchor of the sentence which carries the most semantic of the sentence. The proposed model


depends on part of speech (POS), the partial order of words in the sentence and the words’ senses. The


results by Precision and Recall are promising and benchmarks show that the proposed method improves


the quality of the retrieved matched sentences.

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